cs.AI updates on arXiv.org 07月22日 12:44
A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script
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本文探讨了阿姆哈拉语NLP中同音字归一化对模型性能和跨语言迁移的影响,提出后推理归一化干预策略,提高模型性能并保留语言特征。

arXiv:2507.15142v1 Announce Type: cross Abstract: Homophone normalization, where characters that have the same sound in a writing script are mapped to one character, is a pre-processing step applied in Amharic Natural Language Processing (NLP) literature. While this may improve performance reported by automatic metrics, it also results in models that are not able to understand different forms of writing in a single language. Further, there might be impacts in transfer learning, where models trained on normalized data do not generalize well to other languages. In this paper, we experiment with monolingual training and cross-lingual transfer to understand the impacts of normalization on languages that use the Ge'ez script. We then propose a post-inference intervention in which normalization is applied to model predictions instead of training data. With our simple scheme of post-inference normalization, we show that we can achieve an increase in BLEU score of up to 1.03 while preserving language features in training. Our work contributes to the broader discussion on technology-facilitated language change and calls for more language-aware interventions.

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阿姆哈拉语NLP 同音字归一化 跨语言迁移 后推理归一化 语言特征
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